Advances in Artificial Intelligence: 13th Biennial by Jonathan Schaeffer, Aske Plaat (auth.), Howard J. Hamilton

By Jonathan Schaeffer, Aske Plaat (auth.), Howard J. Hamilton (eds.)

This booklet constitutes the refereed court cases of the thirteenth Biennial convention of the Canadian Society for Computational reviews of Intelligence, AI 2000, held in Montreal, Quebec, Canada, in might 2000. The 25 revised complete papers offered including 12 10-page posters have been conscientiously reviewed and chosen from greater than 70 submissions. The papers are equipped in topical sections on video games and constraint pride; average language processing; wisdom illustration; AI purposes; laptop studying and information mining; making plans, theorem proving, and synthetic existence; and neural networks.

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Extra info for Advances in Artificial Intelligence: 13th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI 2000 Montéal, Quebec, Canada, May 14–17, 2000 Proceedings

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Proof: The algorithm attempts to construct a par-consistent extension of a tupi . tupi is a partial assignment on a variable set Vti . Consider an extension 36 Sivakumar Nagarajan, Scott Goodwin, and Abdul Sattar to this tuple, tupnew =tupi 1 tupi+1 . This extension is an assignment on a variable set Vtupnew = Vti ∪ Vtupi+1 . By definition of the par-consistent extension, for all variables in Vti ∩ Vtupi+1 , tupnew [Vti ∩ Vtupi+1 ] = tupi [Vti ∩ Vtupi+1 ]. , all previous assignments to variables remain unchanged, and hence all constraints that were previously satisfied remain 2 satisfied in the par-consistent extension.

A noun phrase is chosen based on its length, its frequency and the frequency of its head noun. Noun phrases are extracted from a text using a base noun phrase skimmer and an off-the-shelf online dictionary. Experiments involving human judges reveal several interesting results: the simple noun phrase-based system performs roughly as well as a state-of-the-art, corpus-trained keyphrase extractor; ratings for individual keyphrases do not necessarily correlate with ratings for sets of keyphrases for a document; agreement among unbiased judges on the keyphrase rating task is poor.

A node in the CDBT search tree corresponds to a tuple (an instantiation to a set of variables). Internal nodes are consistent tuples, while leaf nodes correspond either to tuples that cannot be extended further thereby causing CDBT to backtrack (represented here by X), or tuples that are solutions. Consistent nodes are again represented by circles. Each level of the tree represents the choice of a constraint to extend a tuple. Each node represents the specific extension of a tuple by joining it with another tuple from the selected constraint (This is using the 1 operator).

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